Radiographic Progression in Rheumatoid Arthritis: Does It Still Happen and Does It Matter?
Bibliographic record
Abstract
Radiographic progression (RP) has been an important objective outcome for assessing the comparative efficacy of therapies in clinical trials. It is being increasingly reported in observational studies of clinical care. RP is typically reported as a change in a modified version of the Sharp score (SS)1. Reported rates and extent of RP are much lower in patients with rheumatoid arthritis (RA) treated intensively, to a target of low disease activity or remission while receiving disease-modifying antirheumatic drugs (DMARD), including more effective doses of methotrexate (MTX)2. However, persisting swelling of ≥ 2 (of 28) joints is associated with further RP (defined as a change > 0.5 over 1 year) using SS scoring methods3. RP continues throughout the course of RA4, although less often for patients who are in remission more often5. RP reporting varies widely, using different cutoffs such as the smallest detectable change (SDC) to describe “rapid radiographic progression” (RRP)6. Thus rates of RP will vary depending on study design, patients studied, disease activity, and intensity of treatment interventions. In this issue of The Journal , Ørnbjerg, et al publish additional results from the Danish Biologics Registry (DANBIO) on the rate and extent of RP in patients with serial hand radiographs using tumor necrosis factor (TNF) inhibitor (TNFi) therapy over an average of 1.5 years7. Ørnbjerg, et al aimed to understand the effect on RP of drug switching and withdrawal. Prior analyses had shown that the extent and rate of RP dropped significantly once patients failing DMARD switched to TNFi therapy8. DANBIO patients had longstanding disease of 9 years, higher than usual rates of smoking (38%), high C-reactive protein (CRP) levels for DMARD and steroid-treated patients, and 82% in this study had erosive disease. … Address correspondence to Dr. Bykerk. E-mail: bykerkv{at}hss.edu
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".